The proposal of this research is to present a new strategy of drive, called of Neural Vectorial Control, using a Multilayer Neural Network acting as a direct adaptive controller, that is based on the minimization of the error between the actual position vector and the vector of reference position. Two strategies of control are shown. The first strategy is based on the use of position neural controllers of independent axes. The second strategy, presented as the main contribution of this paper, is based on the use of the vectorial neural controller. The strategy proposal in this research is differentiated of the well known tracking controllers for not possessing individual closed loops of control for each axis. The XY table used for validation is a structure of two degrees of freedom, that it is considered as a manipulator with detached axes. Experimental and simulated results show the superior performance of the vectorial neural control. A lesser time of processing in the use of an only Neural Nework is an additional advantage of the use of the vectorial neural controller in relation to the independent controllers.
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In the recent years, several works have been proposed with an approach to the use of semantics to improve the process of discovering geographic resources offered by spatial data infrastructures. However, semantic queries may return a large number of results, what causes the necessity for efficient ways to evaluate the relevance of each result retrieved. This paper proposes a framework that uses ontologies and thematic relevance to suggest a measurement that allows evaluating how relevant is each resource offered by the infrastructure to the user's query. This feature allows the results retrieved in a query to be organized through a ranking, in such a way that the most relevant resources are presented to the user first.
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